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The stable continuation of trajectories among different targets has always been the key to the tracking performance of multi-object tracking (MOT) tasks. If features of the target are aggregated and classified simply, the discriminant features of the target will be ignored. This will affect the robustness of the trajectory generated by the model. Meanwhile, many popular models are keen to execute detection and feature extraction tasks in parallel. But these two tasks will conflict with each other when optimized respectively. Therefore, we propose our tracker MOTFR to solve the above problems. In this paper, we propose a Locally Shared Information Decoupling Module (LSIDM) to reduce task optimization conflicts while ensuring the necessary information sharing. Meanwhile, a feature recoding module for deep extraction of identity discriminative features is proposed, which is called the Feature Purification Module (FPM). By combining LSIDM and FPM modules, the model utilizes the discriminative appearance features to guide the optimization of detection and further improves the performance of our model. To solve the problem of targets disappearing due to various abnormal occlusion, a Short-term Trajectory Online Complement Strategy (STOCS) is proposed to realize the trajectories continuation of these targets in the tracking stage. Through sufficient experiments, we demonstrate the superior performance of our MOTFR, which guarantees high-quality detection while achieving the stability of the target trajectory. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation (UDA) person re-identification (ReID) faces enormous challenges due to the severe shift between the source and target domains, as well as the dramatic variations within the target domain. In this paper, to address these issues, we propose a multi-loss gap minimization learning (MGML) approach for UDA person ReID. Firstly, we introduce the part model to learn discriminative patch features and design a Patch-based Part Ignoring (PPI) loss to select reliable instances for the efficient learning of the part model. Then, given the gap that typically occurs because of the inter-domain shift and intra-domain variations, a Gap-based Minimum Camera Discrepancy (G-MCD) loss is proposed. Specifically, in terms of the inter-domain, we propose to leverage the tracklet and camera information to label each distance vector, and accordingly align pair-wise distance distributions to bridge the inter-domain gap. As for the intra-domain, to alleviate the biased search, we propose to perform the mined neighborhood for intra-camera and inter-camera separately to optimize matching results by exploring neighborhood relations more deeply. Finally, experimental results on three challenging datasets demonstrate that applying our method to unlabeled target domain outperforms current UDA methods for person ReID. [ABSTRACT FROM AUTHOR]

Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality pedestrian retrieval task which aims to match person images between the visible and infrared modality of the same identity. Existing methods usually adopt two-stream network to solve cross-modality gap, but they ignore the pixel-level discrepancy between the visible and infrared images. Some methods introduce auxiliary modalities in the network, but they lack powerful constraints on the feature distribution of multiple modalities. In this letter, we propose a Dynamic Center Aggregation (DCA) loss with mixed modality for VI-ReID. Concretely, we employ a mixed modality as a bridge between the visible and infrared modality, reducing the difference of the two modalities at the pixel-level. The mixed modality is generated by a Dual-modality Feature Mixer (DFM), which combines the features of visible and infrared images. Moreover, we dynamically adjust the relative distance across multi-modality through DCA loss, which is conducive to explore the modality-invariant feature. We evaluate the proposed method on two public available VI-ReID datasets (SYSU-MM01 and RegDB). Experimental results demonstrate that our method achieves competitive performance. [ABSTRACT FROM AUTHOR]

The launching of low-cost depth sensors opens up new potentials for RGB-D-based human action recognition. However, most of current RGB-D-based methods simply fuse multimodal features in a holistic manner and ignore the latent connections among different modalities. In this paper, we propose a multimodal correlative representation learning (MCRL) model for human action recognition from RGB-D videos. Specifically, we propose a spatio-temporal pyramid Fourier HOG feature (STPF-HOG) to capture local dynamic patterns around each human joint, which integrates both spatial arrangement and temporal structures. The proposed MCRL model utilizes multimodal data (skeleton, depth, and RGB), and learns shared structures among different modalities. The original low-level features are effectively compressed and projected to a latent subspace. Then, the discriminative shared features are learned by a supervised fashion. We formulate both subspace learning and shared features mining in a modified multi-task learning framework and solve the formulation using an iterative optimization algorithm. To perform computationally efficient action recognition, a robust collaborative representation classifier is presented by introducing a weight regularization matrix. Experimental results on three action datasets demonstrate that the proposed method leads to a more favorable performance compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]